Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Abstract

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.

Keystroke Dynamics Patterns While Writing Positive and Negative Opinions

“…We wanted the sample to be as homogenous as possible, as we did not want to analyze the influence of age on keystroke patterns, considering it as a confounding variable. Differences in keystroke dynamics among various age groups have been investigated in a number of research studies focusing on recognizing age on the basis of keystroke dynamics [ 23 , 24 , 25 ]. The data were anonymized.…”

Battle Ground: Data Collection and Labeling of CTF Games to Understand Human Cyber Operators

“…Finally, we take inspiration from the field of keystroke dynamics. A body of research has emerged using the timing intervals between key presses as a away to authenticate and identify individual users [23]. This technique has many applications, including identification of individual programmers [12].…”

“…This is used for indexing purposes and can further expedite the tagging tasks which are next described. Additionally, the column labeled accuracy calculates 𝐴 = 23 26 as the ratio of characters in the entered buffer to the total number of keystrokes.…”

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“…Tsimperidis and Arampatzis [31] attempted to identify characteristics of users, such as gender, age, and handedness, using KD features and a rotation forest classifier, achieving high accuracy rates in user profiling. Tsimperidis et al [32] used keystroke durations and diagram latencies extracted from a dataset to develop a system that could accurately distinguish the age group of an unknown user. Roy et al [33] proposed a KD-based indicator for Parkinson's disease screening at home, using ensemble learning and addressing key hypotheses related to the screening process to enhance the accuracy and effectiveness of the method.…”